18 citations · 21 across the 3 of their papers we have counts for
3 papers
cs.CV2022
Open-Set Semi-Supervised Learning for 3D Point Cloud Understanding
Xian Shi, Xun Xu, Wanyue Zhang +3
Semantic understanding of 3D point cloud relies on learning models with massively annotated data, which, in many cases, are expensive or difficult to collect. This has led to an em…
cs.CV2021★ 18 cited
Label-Efficient Point Cloud Semantic Segmentation: An Active Learning Approach
Xian Shi, Xun Xu, Ke Chen +3
Deep learning models are the state-of-the-art methods for semantic point cloud segmentation, the success of which relies on the availability of large-scale annotated datasets. Howe…
cs.CV2020★ 3 cited
CAD-PU: A Curvature-Adaptive Deep Learning Solution for Point Set Upsampling
Jiehong Lin, Xian Shi, Yuan Gao +2
Point set is arguably the most direct approximation of an object or scene surface, yet its practical acquisition often suffers from the shortcoming of being noisy, sparse, and poss…